Regarding hour of the day, rush hours are clearly associated with higher number of hourly bike rides. There is a peak at at eight o’clock in the morning and a (less pronounced) one in the afternoon. It is also visible that hour of the day interacts with other variables. First, the average PDP line is misleading, as there is a dense area of parallel lines indicating higher bike rides, as well as a high density at zero bike rides (i.e., the average PDP line obscures these two clusters). Second, There are a few lines showing a different pattern, starting to pick up only after the rush hour and steadily rising until the afternoon. This might be the interaction with the day of the week (see section Interactions; see also figure caption for more details about PDP and ICE plots).
Figure: Partial dependence plots (PDP) [[1,2] show the influence of a feature by detailing the effect on the target value (\(y\)-axis) over the whole range of the feature (\(x\)-axis). That is, the thick, dark blue line in the plot shows the main effect of a feature.
In addition, individual conditional expectation (ICE) plots [2] quantify the extent of interactions that exist with other variables. The fine, light blue lines in the plot show predictions at different levels of the feature in question. If they are parallel, there are no interactions with other features. If they are not and show varying slopes compared to the main affect, the feature interacts with other features.
In addition, the plot shows the distribution of the data over the whole feature range as dark blue ticks below the \(y\)-axis. For hour of the day, this is not very interesting (only full hours), but it shows areas with sparse data for other features.